move loraplus args from args to network_args, simplify log lr desc

library/train_util.py

@@ -2920,9 +2920,6 @@ def add_optimizer_arguments(parser: argparse.ArgumentParser):
         default=1,
         help="Polynomial power for polynomial scheduler / polynomialスケジューラでのpolynomial power",
     )
-    parser.add_argument("--loraplus_lr_ratio", default=None, type=float, help="LoRA+ learning rate ratio")
-    parser.add_argument("--loraplus_unet_lr_ratio", default=None, type=float, help="LoRA+ UNet learning rate ratio")
-    parser.add_argument("--loraplus_text_encoder_lr_ratio", default=None, type=float, help="LoRA+ text encoder learning rate ratio")
 
 
 def add_training_arguments(parser: argparse.ArgumentParser, support_dreambooth: bool):

networks/lora.py

@@ -490,6 +490,14 @@ def create_network(
         varbose=True,
     )
 
+    loraplus_lr_ratio = kwargs.get("loraplus_lr_ratio", None)
+    loraplus_unet_lr_ratio = kwargs.get("loraplus_unet_lr_ratio", None)
+    loraplus_text_encoder_lr_ratio = kwargs.get("loraplus_text_encoder_lr_ratio", None)
+    loraplus_lr_ratio = float(loraplus_lr_ratio) if loraplus_lr_ratio is not None else None
+    loraplus_unet_lr_ratio = float(loraplus_unet_lr_ratio) if loraplus_unet_lr_ratio is not None else None
+    loraplus_text_encoder_lr_ratio = float(loraplus_text_encoder_lr_ratio) if loraplus_text_encoder_lr_ratio is not None else None
+    network.set_loraplus_lr_ratio(loraplus_lr_ratio, loraplus_unet_lr_ratio, loraplus_text_encoder_lr_ratio)
+
     if up_lr_weight is not None or mid_lr_weight is not None or down_lr_weight is not None:
         network.set_block_lr_weight(up_lr_weight, mid_lr_weight, down_lr_weight)
 
@@ -1033,18 +1041,27 @@ class LoRANetwork(torch.nn.Module):
 
         return lr_weight
 
+    def set_loraplus_lr_ratio(self, loraplus_lr_ratio, loraplus_unet_lr_ratio, loraplus_text_encoder_lr_ratio):
+        self.loraplus_lr_ratio = loraplus_lr_ratio
+        self.loraplus_unet_lr_ratio = loraplus_unet_lr_ratio
+        self.loraplus_text_encoder_lr_ratio = loraplus_text_encoder_lr_ratio
+
     # 二つのText Encoderに別々の学習率を設定できるようにするといいかも
-    def prepare_optimizer_params(
-        self,
-        text_encoder_lr,
-        unet_lr,
-        default_lr,
-        text_encoder_loraplus_ratio=None,
-        unet_loraplus_ratio=None,
-        loraplus_ratio=None
-    ):
+    def prepare_optimizer_params(self, text_encoder_lr, unet_lr, default_lr):
+        # TODO warn if optimizer is not compatible with LoRA+ (but it will cause error so we don't need to check it here?)
+        # if (
+        #     self.loraplus_lr_ratio is not None
+        #     or self.loraplus_text_encoder_lr_ratio is not None
+        #     or self.loraplus_unet_lr_ratio is not None
+        # ):
+        #     assert (
+        #         optimizer_type.lower() != "prodigy" and "dadapt" not in optimizer_type.lower()
+        #     ), "LoRA+ and Prodigy/DAdaptation is not supported / LoRA+とProdigy/DAdaptationの組み合わせはサポートされていません"
+
         self.requires_grad_(True)
+
         all_params = []
+        lr_descriptions = []
 
         def assemble_params(loras, lr, ratio):
             param_groups = {"lora": {}, "plus": {}}
@@ -1056,6 +1073,7 @@ class LoRANetwork(torch.nn.Module):
                         param_groups["lora"][f"{lora.lora_name}.{name}"] = param
 
             params = []
+            descriptions = []
             for key in param_groups.keys():
                 param_data = {"params": param_groups[key].values()}
 
@@ -1069,20 +1087,22 @@ class LoRANetwork(torch.nn.Module):
                         param_data["lr"] = lr
 
                 if param_data.get("lr", None) == 0 or param_data.get("lr", None) is None:
-                    print("NO LR skipping!")
+                    logger.info("NO LR skipping!")
                     continue
 
                 params.append(param_data)
+                descriptions.append("plus" if key == "plus" else "")
 
-            return params
+            return params, descriptions
 
         if self.text_encoder_loras:
-            params = assemble_params(
+            params, descriptions = assemble_params(
                 self.text_encoder_loras,
                 text_encoder_lr if text_encoder_lr is not None else default_lr,
-                text_encoder_loraplus_ratio or loraplus_ratio
+                self.loraplus_text_encoder_lr_ratio or self.loraplus_lr_ratio,
             )
             all_params.extend(params)
+            lr_descriptions.extend(["textencoder" + (" " + d if d else "") for d in descriptions])
 
         if self.unet_loras:
             if self.block_lr:
@@ -1096,22 +1116,24 @@ class LoRANetwork(torch.nn.Module):
 
                 # blockごとにパラメータを設定する
                 for idx, block_loras in block_idx_to_lora.items():
-                    params = assemble_params(
+                    params, descriptions = assemble_params(
                         block_loras,
                         (unet_lr if unet_lr is not None else default_lr) * self.get_lr_weight(block_loras[0]),
-                        unet_loraplus_ratio or loraplus_ratio
+                        self.loraplus_unet_lr_ratio or self.loraplus_lr_ratio,
                     )
                     all_params.extend(params)
+                    lr_descriptions.extend([f"unet_block{idx}" + (" " + d if d else "") for d in descriptions])
 
             else:
-                params = assemble_params(
+                params, descriptions = assemble_params(
                     self.unet_loras,
                     unet_lr if unet_lr is not None else default_lr,
-                    unet_loraplus_ratio or loraplus_ratio
+                    self.loraplus_unet_lr_ratio or self.loraplus_lr_ratio,
                 )
                 all_params.extend(params)
+                lr_descriptions.extend(["unet" + (" " + d if d else "") for d in descriptions])
 
-        return all_params
+        return all_params, lr_descriptions
 
     def enable_gradient_checkpointing(self):
         # not supported

train_network.py

@@ -53,7 +53,15 @@ class NetworkTrainer:
 
     # TODO 他のスクリプトと共通化する
     def generate_step_logs(
-        self, args: argparse.Namespace, current_loss, avr_loss, lr_scheduler, keys_scaled=None, mean_norm=None, maximum_norm=None
+        self,
+        args: argparse.Namespace,
+        current_loss,
+        avr_loss,
+        lr_scheduler,
+        lr_descriptions,
+        keys_scaled=None,
+        mean_norm=None,
+        maximum_norm=None,
     ):
         logs = {"loss/current": current_loss, "loss/average": avr_loss}
 
@@ -63,70 +71,27 @@ class NetworkTrainer:
             logs["max_norm/max_key_norm"] = maximum_norm
 
         lrs = lr_scheduler.get_last_lr()
+        for i, lr in enumerate(lrs):
+            if lr_descriptions is not None:
+                lr_desc = lr_descriptions[i]
+            else:
+                idx = i - (0 if args.network_train_unet_only else -1)
+                if idx == -1:
+                    lr_desc = "textencoder"
+                else:
+                    if len(lrs) > 2:
+                        lr_desc = f"group{idx}"
+                    else:
+                        lr_desc = "unet"
 
-        if len(lrs) > 4:
-            idx = 0
-            if not args.network_train_unet_only:
-                logs["lr/textencoder"] = float(lrs[0])
-                idx = 1
+            logs[f"lr/{lr_desc}"] = lr
 
-            for i in range(idx, len(lrs)):
-                lora_plus = ""
-                group_id = i
-
-                if args.loraplus_lr_ratio is not None or args.loraplus_unet_lr_ratio is not None:
-                    lora_plus = '_lora+' if i % 2 == 1 else '' 
-                    group_id = int((i / 2) + (i % 2 + 0.5))
-
-                logs[f"lr/group{group_id}{lora_plus}"] = float(lrs[i])
             if args.optimizer_type.lower().startswith("DAdapt".lower()) or args.optimizer_type.lower() == "Prodigy".lower():
-                    logs[f"lr/d*lr/group{group_id}{lora_plus}"] = (
+                # tracking d*lr value
+                logs[f"lr/d*lr/{lr_desc}"] = (
                     lr_scheduler.optimizers[-1].param_groups[i]["d"] * lr_scheduler.optimizers[-1].param_groups[i]["lr"]
                 )
 
-        else:
-            if args.network_train_text_encoder_only:
-                if args.loraplus_lr_ratio is not None or  args.loraplus_text_encoder_lr_ratio is not None:
-                    logs["lr/textencoder"] = float(lrs[0])
-                    logs["lr/textencoder_lora+"] = float(lrs[1])
-                else:
-                    logs["lr/textencoder"] = float(lrs[0])
-                
-            elif args.network_train_unet_only:
-                if args.loraplus_lr_ratio is not None or args.loraplus_unet_lr_ratio is not None:
-                    logs["lr/unet"] = float(lrs[0])
-                    logs["lr/unet_lora+"] = float(lrs[1])
-                else:
-                    logs["lr/unet"] = float(lrs[0])
-            else:
-                if len(lrs) == 2:
-                    if args.loraplus_text_encoder_lr_ratio is not None and args.loraplus_unet_lr_ratio is None:
-                        logs["lr/textencoder"] = float(lrs[0])
-                        logs["lr/textencoder_lora+"] = float(lrs[1])
-                    elif args.loraplus_unet_lr_ratio is not None and args.loraplus_text_encoder_lr_ratio is None:
-                        logs["lr/unet"] = float(lrs[0])
-                        logs["lr/unet_lora+"] = float(lrs[1])
-                    elif args.loraplus_unet_lr_ratio is None and args.loraplus_text_encoder_lr_ratio is None and args.loraplus_lr_ratio is not None:
-                        logs["lr/all"] = float(lrs[0])
-                        logs["lr/all_lora+"] = float(lrs[1])
-                    else:
-                        logs["lr/textencoder"] = float(lrs[0])
-                        logs["lr/unet"] = float(lrs[-1])
-                elif len(lrs) == 4:
-                    logs["lr/textencoder"] = float(lrs[0])
-                    logs["lr/textencoder_lora+"] = float(lrs[1])
-                    logs["lr/unet"] = float(lrs[2])
-                    logs["lr/unet_lora+"] = float(lrs[3])
-                else:
-                    logs["lr/all"] = float(lrs[0])
-
-            if (
-                args.optimizer_type.lower().startswith("DAdapt".lower()) or args.optimizer_type.lower() == "Prodigy".lower()
-            ):  # tracking d*lr value of unet.
-                logs["lr/d*lr"] = (
-                    lr_scheduler.optimizers[-1].param_groups[0]["d"] * lr_scheduler.optimizers[-1].param_groups[0]["lr"]
-                )
-
         return logs
 
     def assert_extra_args(self, args, train_dataset_group):
@@ -358,6 +323,7 @@ class NetworkTrainer:
         network.apply_to(text_encoder, unet, train_text_encoder, train_unet)
 
         if args.network_weights is not None:
+            # FIXME consider alpha of weights
             info = network.load_weights(args.network_weights)
             accelerator.print(f"load network weights from {args.network_weights}: {info}")
 
@@ -373,20 +339,23 @@ class NetworkTrainer:
 
         # 後方互換性を確保するよ
         try:
-            trainable_params = network.prepare_optimizer_params(args.text_encoder_lr, args.unet_lr, args.learning_rate, args.loraplus_text_encoder_lr_ratio, args.loraplus_unet_lr_ratio, args.loraplus_lr_ratio)
+            results = network.prepare_optimizer_params(args.text_encoder_lr, args.unet_lr, args.learning_rate)
+            if type(results) is tuple:
+                trainable_params = results[0]
+                lr_descriptions = results[1]
+            else:
+                trainable_params = results
+                lr_descriptions = None
         except TypeError:
-            accelerator.print(
-                "Deprecated: use prepare_optimizer_params(text_encoder_lr, unet_lr, learning_rate) instead of prepare_optimizer_params(text_encoder_lr, unet_lr)"
-            )
+            # accelerator.print(
+            #     "Deprecated: use prepare_optimizer_params(text_encoder_lr, unet_lr, learning_rate) instead of prepare_optimizer_params(text_encoder_lr, unet_lr)"
+            # )
             trainable_params = network.prepare_optimizer_params(args.text_encoder_lr, args.unet_lr)
+            lr_descriptions = None
+        print(lr_descriptions)
 
         optimizer_name, optimizer_args, optimizer = train_util.get_optimizer(args, trainable_params)
 
-        if args.loraplus_lr_ratio is not None or args.loraplus_text_encoder_lr_ratio is not None or args.loraplus_unet_lr_ratio is not None:
-            assert (
-                (optimizer_name != "Prodigy" and "DAdapt" not in optimizer_name)
-            ), "LoRA+ and Prodigy/DAdaptation is not supported"
-
         # dataloaderを準備する
         # DataLoaderのプロセス数：0 は persistent_workers が使えないので注意
         n_workers = min(args.max_data_loader_n_workers, os.cpu_count())  # cpu_count or max_data_loader_n_workers
@@ -992,7 +961,9 @@ class NetworkTrainer:
                     progress_bar.set_postfix(**{**max_mean_logs, **logs})
 
                 if args.logging_dir is not None:
-                    logs = self.generate_step_logs(args, current_loss, avr_loss, lr_scheduler, keys_scaled, mean_norm, maximum_norm)
+                    logs = self.generate_step_logs(
+                        args, current_loss, avr_loss, lr_scheduler, lr_descriptions, keys_scaled, mean_norm, maximum_norm
+                    )
                     accelerator.log(logs, step=global_step)
 
                 if global_step >= args.max_train_steps:
@@ -1143,6 +1114,9 @@ def setup_parser() -> argparse.ArgumentParser:
         action="store_true",
         help="do not use fp16/bf16 VAE in mixed precision (use float VAE) / mixed precisionでも fp16/bf16 VAEを使わずfloat VAEを使う",
     )
+    # parser.add_argument("--loraplus_lr_ratio", default=None, type=float, help="LoRA+ learning rate ratio")
+    # parser.add_argument("--loraplus_unet_lr_ratio", default=None, type=float, help="LoRA+ UNet learning rate ratio")
+    # parser.add_argument("--loraplus_text_encoder_lr_ratio", default=None, type=float, help="LoRA+ text encoder learning rate ratio")
     return parser